[Feature] Support JIT set kv cache (#16273)
This commit is contained in:
84
python/sglang/jit_kernel/kvcache.py
Normal file
84
python/sglang/jit_kernel/kvcache.py
Normal file
@@ -0,0 +1,84 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
|
||||
from sglang.jit_kernel.utils import (
|
||||
cache_once,
|
||||
is_arch_support_pdl,
|
||||
load_jit,
|
||||
make_cpp_args,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from tvm_ffi.module import Module
|
||||
|
||||
|
||||
@cache_once
|
||||
def _jit_kvcache_module(row_bytes: int) -> Module:
|
||||
args = make_cpp_args(row_bytes, is_arch_support_pdl())
|
||||
return load_jit(
|
||||
"kvcache",
|
||||
*args,
|
||||
cuda_files=["elementwise/kvcache.cuh"],
|
||||
cuda_wrappers=[("store_cache", f"StoreKVCacheKernel<{args}>::run")],
|
||||
)
|
||||
|
||||
|
||||
@cache_once
|
||||
def can_use_store_cache(size: int) -> bool:
|
||||
logger = logging.getLogger(__name__)
|
||||
if size % 4 != 0:
|
||||
logger.warning(
|
||||
f"Unsupported row_bytes={size} for JIT KV-Cache kernel:"
|
||||
" must be multiple of 4"
|
||||
)
|
||||
return False
|
||||
try:
|
||||
_jit_kvcache_module(size)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Failed to load JIT KV-Cache kernel " f"with row_bytes={size}: {e}"
|
||||
)
|
||||
return False
|
||||
|
||||
|
||||
def store_cache(
|
||||
k: torch.Tensor,
|
||||
v: torch.Tensor,
|
||||
k_cache: torch.Tensor,
|
||||
v_cache: torch.Tensor,
|
||||
indices: torch.Tensor,
|
||||
*,
|
||||
row_bytes: int = 0,
|
||||
num_split: int = 0, # can be tuned for performance
|
||||
) -> None:
|
||||
"""Store key and value tensors into KV cache at specified indices.
|
||||
|
||||
Args:
|
||||
k (torch.Tensor): Key tensor of shape (batch_size, H * D).
|
||||
v (torch.Tensor): Value tensor of shape (batch_size, H * D).
|
||||
k_cache (torch.Tensor): Key cache tensor of shape (num_pages, H * D).
|
||||
v_cache (torch.Tensor): Value cache tensor of shape (num_pages, H * D).
|
||||
indices (torch.Tensor): Indices tensor of shape (batch_size,).
|
||||
"""
|
||||
row_bytes = row_bytes or k.shape[-1] * k.element_size()
|
||||
module = _jit_kvcache_module(row_bytes)
|
||||
if num_split <= 0:
|
||||
if row_bytes % 2048 == 0:
|
||||
num_split = 4
|
||||
elif row_bytes % 1024 == 0:
|
||||
num_split = 2
|
||||
else:
|
||||
num_split = 1
|
||||
module.store_cache(
|
||||
k,
|
||||
v,
|
||||
k_cache,
|
||||
v_cache,
|
||||
indices,
|
||||
num_split,
|
||||
)
|
||||
Reference in New Issue
Block a user